Bayescenv confirm convergence
Devon DeRaad
8/26/2020
library(coda)## Warning: package 'coda' was built under R version 3.5.2library(lattice)2R hum
#read in trace file
chain <- read.table("~/Desktop/bayescenv.results/1.hum.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.562
## Fst2 passed 1 0.858
## Fst3 passed 1 0.374
## Fst4 passed 1 0.178
## Fst5 passed 1 0.885
## Fst6 passed 1 0.915
## Fst7 passed 1 0.363
## Fst8 passed 1001 0.118
## Fst9 passed 1 0.360
## Fst10 passed 1 0.988
## Fst11 passed 1 0.689
## Fst12 passed 1 0.461
## Fst13 passed 1 0.930
## Fst14 passed 1 0.470
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.0197 1.88e-06
## Fst2 passed 0.0823 5.12e-06
## Fst3 passed 0.0464 3.32e-06
## Fst4 passed 0.0403 2.82e-06
## Fst5 passed 0.1553 7.99e-06
## Fst6 passed 0.4601 1.58e-05
## Fst7 passed 0.3979 1.58e-05
## Fst8 passed 0.0153 1.80e-06
## Fst9 passed 0.0133 1.58e-06
## Fst10 passed 0.0121 1.58e-06
## Fst11 passed 0.5015 1.52e-05
## Fst12 passed 0.1194 6.77e-06
## Fst13 passed 0.0130 2.20e-06
## Fst14 passed 0.0191 2.46e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2536.325 2278.556 2592.993 2425.225 2499.479 3098.282 3334.913 2028.386
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 1904.999 1798.632 2851.276 2643.240 4013.362 4437.809plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.00000000 1.0000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.27785281 0.3301838318 0.316823815 0.346584534 0.303192931
## Lag 50 0.02176418 0.0285955203 0.029779557 0.024448861 -0.001244466
## Lag 100 -0.02381834 -0.0080026490 -0.019820165 0.011712968 -0.009972557
## Lag 500 -0.01217865 -0.0006909855 0.001081149 -0.002638427 -0.003595192
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.000000000 1.000000000 1.00000000 1.00000000
## Lag 10 0.205152419 0.201427261 0.387551779 0.39613767 0.40573444
## Lag 50 0.017621393 0.008660413 0.050865608 0.04121815 0.06165115
## Lag 100 0.002190546 0.004839251 -0.033427962 0.02466069 -0.01870449
## Lag 500 0.024612240 -0.001941502 -0.002418183 -0.02733780 -0.01457587
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.00000000 1.00000000 1.000000000
## Lag 10 0.273493229 0.30816461 0.08363183 0.059368585
## Lag 50 0.028631380 0.02434193 0.01974170 -0.015651225
## Lag 100 -0.008380324 0.01026715 0.03093587 -0.004660783
## Lag 500 0.032197514 0.02002391 -0.02071807 -0.001949634levelplot(t(autocorr.diag(chain)))
plot(chain)
2L hum
#read in trace file
chain <- read.table("~/Desktop/bayescenv.results/2.hum.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.3992
## Fst2 passed 501 0.1754
## Fst3 passed 1 0.5333
## Fst4 passed 1 0.3280
## Fst5 passed 1 0.3243
## Fst6 passed 1 0.8281
## Fst7 passed 1 0.1552
## Fst8 passed 1 0.1717
## Fst9 passed 1 0.8562
## Fst10 passed 1 0.0757
## Fst11 passed 1 0.7749
## Fst12 passed 1 0.1924
## Fst13 passed 1 0.7310
## Fst14 passed 1 0.1980
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.0752 6.78e-06
## Fst2 passed 0.1036 9.18e-06
## Fst3 passed 0.0784 6.65e-06
## Fst4 passed 0.0533 4.89e-06
## Fst5 passed 0.1085 7.02e-06
## Fst6 passed 0.4365 1.82e-05
## Fst7 passed 0.3588 1.80e-05
## Fst8 passed 0.0685 5.68e-06
## Fst9 passed 0.0106 1.80e-06
## Fst10 passed 0.0205 2.43e-06
## Fst11 passed 0.4144 1.73e-05
## Fst12 passed 0.1499 1.08e-05
## Fst13 passed 0.0115 2.48e-06
## Fst14 passed 0.0237 3.45e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2147.361 1896.082 2305.720 2008.452 2897.208 3369.080 3643.259 2217.398
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 1735.289 2213.279 2899.372 2369.985 4002.273 3874.588plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.0000000000 1.0000000000 1.000000000 1.000000000
## Lag 10 0.352366462 0.3568173671 0.3428252953 0.335424759 0.244970167
## Lag 50 0.018895418 0.0464418197 -0.0005947549 0.052251907 -0.008939186
## Lag 100 0.002776815 -0.0001161271 -0.0046041590 0.004723725 -0.012256176
## Lag 500 -0.014623652 -0.0208739717 -0.0099515620 -0.007372431 0.027290692
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.00000000 1.000000000 1.00000000 1.000000000 1.00000000
## Lag 10 0.19468204 0.156775865 0.35650106 0.372102467 0.35465145
## Lag 50 -0.01041779 -0.029685642 0.00744280 0.052183604 0.02516319
## Lag 100 0.00505018 -0.010919707 -0.01503548 0.007183738 -0.01705470
## Lag 500 0.01151850 -0.006042276 -0.01308004 0.028377252 -0.02215027
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.000000000 1.00000000 1.000000000
## Lag 10 0.265737445 0.316133769 0.08966331 0.126616078
## Lag 50 0.006336527 0.026254019 0.01206323 0.007875863
## Lag 100 -0.013620652 -0.008680995 0.01501339 -0.032948676
## Lag 500 0.022272096 -0.005380903 -0.01207679 0.001023047levelplot(t(autocorr.diag(chain)))
plot(chain)
3R hum
#read in trace file
chain <- read.table("~/Desktop/bayescenv.results/3.hum.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.4584
## Fst2 passed 1 0.8426
## Fst3 passed 1 0.0680
## Fst4 passed 1 0.5784
## Fst5 passed 1 0.1014
## Fst6 passed 1 0.5427
## Fst7 passed 1 0.6595
## Fst8 passed 1 0.4914
## Fst9 passed 1 0.0576
## Fst10 passed 1 0.1126
## Fst11 passed 1 0.6060
## Fst12 passed 1 0.9416
## Fst13 passed 1 0.4354
## Fst14 passed 1 0.8632
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.00351 8.86e-07
## Fst2 passed 0.03514 3.10e-06
## Fst3 passed 0.01651 1.97e-06
## Fst4 passed 0.02279 1.84e-06
## Fst5 passed 0.06877 4.93e-06
## Fst6 passed 0.49868 1.66e-05
## Fst7 passed 0.43627 1.76e-05
## Fst8 passed 0.00685 8.96e-07
## Fst9 passed 0.00377 7.61e-07
## Fst10 passed 0.00422 8.06e-07
## Fst11 passed 0.30289 1.45e-05
## Fst12 passed 0.08663 5.20e-06
## Fst13 passed 0.00565 1.80e-06
## Fst14 passed 0.00613 1.98e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2657.132 2364.465 2319.247 2677.497 2712.399 3674.859 3947.833 2703.998
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2413.677 2635.033 2649.889 3091.481 4406.603 4028.102plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.000000000 1.00000000 1.0000000000 1.000000000
## Lag 10 0.195644022 0.278496457 0.25268574 0.2725073193 0.260215471
## Lag 50 0.034395499 0.037574290 0.04515135 0.0009340822 0.005644524
## Lag 100 0.002619375 0.004852783 -0.01607054 -0.0146651469 -0.009117449
## Lag 500 -0.025735178 -0.014407072 -0.02209774 0.0022165117 0.014865761
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.00000000 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.18109791 0.143995747 0.258364362 0.276924565 0.253671949
## Lag 50 -0.02215939 0.022461553 0.017007402 0.038312157 0.017111411
## Lag 100 0.01161494 -0.003072026 0.008790121 0.009485982 -0.009097463
## Lag 500 -0.01678479 0.011295545 -0.019092348 -0.011615800 -0.026279325
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.00000000 1.00000000 1.000000000 1.000000000
## Lag 10 0.24928017 0.23567882 0.062883747 0.072533073
## Lag 50 -0.00350218 0.02681047 0.004413889 0.020346025
## Lag 100 0.02125469 -0.01113564 -0.003756937 -0.007870545
## Lag 500 -0.01824956 0.03374842 -0.019715862 0.027519696levelplot(t(autocorr.diag(chain)))
plot(chain)
3L hum
#read in trace file
chain <- read.table("~/Desktop/bayescenv.results/4.hum.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.8539
## Fst2 passed 1 0.2826
## Fst3 passed 501 0.2712
## Fst4 passed 1 0.8813
## Fst5 passed 1 0.1483
## Fst6 passed 1 0.2968
## Fst7 passed 1 0.4333
## Fst8 passed 1 0.7840
## Fst9 passed 1 0.4666
## Fst10 passed 1 0.8290
## Fst11 passed 1 0.6421
## Fst12 passed 1 0.6950
## Fst13 passed 1 0.5849
## Fst14 passed 501 0.0824
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.00359 9.26e-07
## Fst2 passed 0.03313 2.98e-06
## Fst3 passed 0.01582 1.95e-06
## Fst4 passed 0.01593 1.43e-06
## Fst5 passed 0.07050 5.11e-06
## Fst6 passed 0.45914 1.84e-05
## Fst7 passed 0.43192 1.93e-05
## Fst8 passed 0.00565 9.00e-07
## Fst9 passed 0.00218 6.89e-07
## Fst10 passed 0.00272 7.64e-07
## Fst11 passed 0.27830 1.49e-05
## Fst12 passed 0.09108 6.37e-06
## Fst13 passed 0.00367 2.03e-06
## Fst14 passed 0.00301 2.14e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3327.687 3199.788 3465.883 3558.458 3418.884 3999.597 4083.657 3024.769
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2854.547 2792.671 3068.050 3158.844 4384.425 4578.899plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.166670971 0.219353313 0.154530365 0.168240326 0.187612928
## Lag 50 0.029963413 -0.002809453 -0.004203943 -0.009584776 0.020442884
## Lag 100 -0.003724688 -0.002795210 0.009796831 0.019001688 -0.013133661
## Lag 500 -0.006402922 0.015530096 -0.009674081 -0.005687835 -0.005066436
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.000000000 1.00000000 1.000000e+00 1.000000000
## Lag 10 0.110963273 0.100680117 0.24595386 2.527282e-01 0.212275967
## Lag 50 -0.006241574 -0.006084364 0.00529668 1.441980e-02 0.023611609
## Lag 100 0.008709438 0.010208802 -0.01553143 -4.126390e-03 0.003643766
## Lag 500 0.012949359 0.012170748 0.01301009 -7.310615e-05 -0.007978216
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.0000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.2004639582 0.225473950 0.064933822 0.043761680
## Lag 50 0.0063005603 -0.017658453 0.010328644 -0.002355145
## Lag 100 0.0006016564 -0.001880100 0.009025304 0.013010390
## Lag 500 -0.0062386807 0.008214764 -0.003762979 0.002940706levelplot(t(autocorr.diag(chain)))
plot(chain)
X hum
#read in trace file
chain <- read.table("~/Desktop/bayescenv.results/5.hum.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.4721
## Fst2 passed 1 0.6473
## Fst3 passed 1 0.8920
## Fst4 passed 1 0.4007
## Fst5 passed 1 0.5914
## Fst6 failed NA 0.0233
## Fst7 passed 1 0.2899
## Fst8 passed 1 0.3187
## Fst9 passed 1 0.5342
## Fst10 passed 1 0.4311
## Fst11 passed 1 0.5918
## Fst12 passed 1 0.1915
## Fst13 passed 1 0.3622
## Fst14 passed 1 0.3300
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.00772 2.17e-06
## Fst2 passed 0.03891 6.62e-06
## Fst3 passed 0.01852 3.88e-06
## Fst4 passed 0.01613 2.61e-06
## Fst5 passed 0.04713 7.15e-06
## Fst6 <NA> NA NA
## Fst7 passed 0.55161 3.43e-05
## Fst8 passed 0.01331 2.29e-06
## Fst9 passed 0.00568 2.07e-06
## Fst10 passed 0.00840 2.24e-06
## Fst11 passed 0.36144 2.77e-05
## Fst12 passed 0.21543 1.70e-05
## Fst13 passed 0.00208 2.88e-06
## Fst14 passed 0.00873 3.66e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2710.102 2547.307 2574.828 3449.611 2842.847 3835.509 4178.982 3527.502
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 1677.179 2103.108 3543.324 4461.509 2425.455 4316.260plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.000000000 1.0000000000 1.000000000 1.00000000
## Lag 10 0.242063462 0.270074865 0.2528946321 0.183293126 0.20129208
## Lag 50 -0.004118146 0.019220039 0.0030041110 0.003067413 0.02701408
## Lag 100 0.011373099 0.002883986 -0.0002327709 -0.015308562 0.02734832
## Lag 500 -0.002605754 -0.003780627 0.0038281603 -0.014544933 -0.01922636
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.0000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.084160590 0.0892469411 0.172482276 0.315789671 0.213787054
## Lag 50 0.001101342 -0.0124873334 -0.028604644 0.069737895 0.030731975
## Lag 100 -0.020274937 -0.0008866355 -0.002903510 0.014594442 0.055926903
## Lag 500 -0.009153420 0.0001812845 0.001913346 0.008810818 0.005781042
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.000000000 1.00000000 1.000000000
## Lag 10 0.126771821 0.080368355 0.25057287 0.073193152
## Lag 50 0.014887426 0.013747917 0.04334636 0.002407015
## Lag 100 0.005346662 -0.007461244 0.01318911 0.011800336
## Lag 500 -0.015038330 0.014212504 0.00596171 0.010077498levelplot(t(autocorr.diag(chain)))
plot(chain)
2R temp
#read in trace file
chain <- read.table("~/Desktop/bayescenv.results/1.temp.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.169
## Fst2 passed 1 0.435
## Fst3 passed 1 0.894
## Fst4 passed 1 0.594
## Fst5 passed 1 0.320
## Fst6 passed 1 0.297
## Fst7 passed 1 0.161
## Fst8 passed 1 0.872
## Fst9 passed 1 0.472
## Fst10 passed 1 0.495
## Fst11 passed 1 0.867
## Fst12 passed 1 0.677
## Fst13 passed 1 0.385
## Fst14 passed 1 0.804
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.0190 1.96e-06
## Fst2 passed 0.0790 5.26e-06
## Fst3 passed 0.0440 3.42e-06
## Fst4 passed 0.0356 2.66e-06
## Fst5 passed 0.1472 7.82e-06
## Fst6 passed 0.4568 1.59e-05
## Fst7 passed 0.3972 1.57e-05
## Fst8 passed 0.0143 1.52e-06
## Fst9 passed 0.0124 1.44e-06
## Fst10 passed 0.0115 1.50e-06
## Fst11 passed 0.5073 1.43e-05
## Fst12 passed 0.1255 7.07e-06
## Fst13 passed 0.0132 2.24e-06
## Fst14 passed 0.0185 2.48e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2392.269 2060.856 2204.002 2269.397 2581.103 2874.476 3433.181 2093.195
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2047.521 1928.916 3081.762 2494.391 4087.099 4090.082plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.0000000000 1.0000000000 1.00000000 1.00000000 1.00000000
## Lag 10 0.3042449196 0.3558172028 0.30004452 0.33669903 0.31888958
## Lag 50 0.0226607365 0.0377087136 0.03335936 0.01152828 0.02714029
## Lag 100 -0.0004451326 -0.0001930717 -0.01294484 -0.01727245 0.02387898
## Lag 500 -0.0188497134 0.0045594343 0.01218184 -0.01119516 0.02446348
## Fst6 Fst7 Fst8 Fst9 Fst10 Fst11
## Lag 0 1.00000000 1.000000000 1.00000000 1.00000000 1.00000000 1.000000000
## Lag 10 0.26974026 0.185598955 0.36592219 0.36888190 0.39712308 0.237165103
## Lag 50 -0.01760238 -0.019246709 0.03042654 0.05206368 0.04475523 -0.008448042
## Lag 100 -0.03434568 0.009499576 0.02303678 0.02085191 -0.02360783 0.003932481
## Lag 500 0.01265510 0.008531358 0.01212723 0.03066081 0.01757296 0.016042067
## Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 10 0.306000161 0.072828293 0.099901986
## Lag 50 0.021721084 0.001836542 -0.005450162
## Lag 100 -0.006710706 0.014873711 0.015755496
## Lag 500 -0.002891925 0.022964007 -0.015662965levelplot(t(autocorr.diag(chain)))
plot(chain)
2L temp
#read in trace file
chain <- read.table("~/Desktop/bayescenv.results/2.temp.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.432
## Fst2 passed 1 0.501
## Fst3 passed 1 0.497
## Fst4 passed 1 0.273
## Fst5 passed 1 0.449
## Fst6 passed 1 0.172
## Fst7 passed 1 0.440
## Fst8 passed 1 0.998
## Fst9 passed 1 0.674
## Fst10 passed 1 0.223
## Fst11 passed 1 0.084
## Fst12 passed 1 0.635
## Fst13 passed 1 0.116
## Fst14 passed 1 0.747
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.07363 6.74e-06
## Fst2 passed 0.09407 7.83e-06
## Fst3 passed 0.07098 6.22e-06
## Fst4 passed 0.04079 4.07e-06
## Fst5 passed 0.09700 6.79e-06
## Fst6 passed 0.42795 1.76e-05
## Fst7 passed 0.35664 1.75e-05
## Fst8 passed 0.05040 4.91e-06
## Fst9 passed 0.00853 1.58e-06
## Fst10 passed 0.01645 2.11e-06
## Fst11 passed 0.43065 1.69e-05
## Fst12 passed 0.17148 1.02e-05
## Fst13 passed 0.01080 2.88e-06
## Fst14 passed 0.02174 3.46e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2053.486 2050.113 2268.026 1960.895 2655.629 3453.408 3556.143 1985.336
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 1666.255 2211.352 3049.465 2858.760 3608.087 3472.619plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.00000000 1.000000000 1.00000000 1.000000000 1.000000000
## Lag 10 0.34392719 0.324671788 0.33499768 0.361904807 0.277468874
## Lag 50 0.02036704 0.025131456 0.03185293 0.019660516 0.017081507
## Lag 100 0.02123328 -0.017644799 -0.01191047 0.023453873 0.002768169
## Lag 500 -0.02781497 -0.005399608 -0.05017545 0.006535658 0.022967335
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.000000000 1.000000000 1.00000000 1.00000000
## Lag 10 0.182761457 0.168556586 0.379933807 0.37244660 0.36487053
## Lag 50 -0.003007605 0.029905644 0.034634111 0.02610361 0.00258225
## Lag 100 -0.010694124 -0.007559885 -0.029791774 0.01836752 -0.02306234
## Lag 500 0.003873643 0.013759884 0.007496159 -0.01970836 0.00929360
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.00000000 1.0000000000 1.000000000 1.000000000
## Lag 10 0.24213026 0.2722802388 0.136834188 0.107347629
## Lag 50 0.01823515 -0.0003246063 0.010121010 0.008700780
## Lag 100 0.01434836 -0.0057425992 0.010499557 0.004908555
## Lag 500 0.01099866 -0.0162238026 0.007167753 0.001207633levelplot(t(autocorr.diag(chain)))
plot(chain)
3R temp
#read in trace file
chain <- read.table("~/Desktop/bayescenv.results/3.temp.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.959
## Fst2 passed 1 0.916
## Fst3 passed 1 0.509
## Fst4 passed 1 0.674
## Fst5 passed 1 0.695
## Fst6 passed 1 0.946
## Fst7 passed 1 0.978
## Fst8 passed 1 0.516
## Fst9 passed 1 0.491
## Fst10 passed 1 0.317
## Fst11 passed 1 0.868
## Fst12 passed 1 0.152
## Fst13 passed 1 0.488
## Fst14 passed 1 0.260
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.00227 8.88e-07
## Fst2 passed 0.02961 2.92e-06
## Fst3 passed 0.01303 1.67e-06
## Fst4 passed 0.01883 1.78e-06
## Fst5 passed 0.05916 4.45e-06
## Fst6 passed 0.49184 1.76e-05
## Fst7 passed 0.43261 1.77e-05
## Fst8 passed 0.00650 8.22e-07
## Fst9 passed 0.00344 6.74e-07
## Fst10 passed 0.00429 7.22e-07
## Fst11 passed 0.31566 1.50e-05
## Fst12 passed 0.09013 5.31e-06
## Fst13 passed 0.00665 2.09e-06
## Fst14 passed 0.00770 1.91e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2415.364 2211.350 2598.957 2379.492 2576.316 3298.200 3493.761 2597.133
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2465.698 2560.458 2523.920 3111.177 3786.290 4498.068plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.00000000 1.000000000 1.000000e+00 1.000000000 1.000000000
## Lag 10 0.23547002 0.300688212 2.666899e-01 0.319618353 0.284876407
## Lag 50 0.02032158 0.019436690 3.118856e-02 0.016181911 0.007829523
## Lag 100 -0.01016892 0.005113932 8.999153e-05 0.003797978 -0.002901539
## Lag 500 -0.00294128 -0.001649672 5.641814e-03 0.027311238 0.008020046
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.0000000000 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.2048889666 0.177140944 0.265238017 0.266942105 0.259940898
## Lag 50 -0.0090468929 -0.024843331 0.006343313 0.022359732 0.024573615
## Lag 100 0.0403602447 0.002693342 -0.014586453 0.009325879 0.012827534
## Lag 500 0.0009769934 0.003137464 -0.013845268 -0.008611015 -0.003415362
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.00000000 1.000000000 1.000000000
## Lag 10 0.290467898 0.23267746 0.058807820 0.052646197
## Lag 50 0.029945399 -0.01204357 0.024608536 0.003921911
## Lag 100 0.007460066 -0.01140690 -0.007267134 -0.013534382
## Lag 500 0.005547759 -0.01424428 -0.010924738 0.001419814levelplot(t(autocorr.diag(chain)))
plot(chain)
3L temp
#read in trace file
chain <- read.table("~/Desktop/bayescenv.results/4.temp.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.931
## Fst2 passed 1 0.349
## Fst3 passed 1 0.428
## Fst4 passed 1 0.228
## Fst5 passed 1 0.419
## Fst6 passed 1 0.529
## Fst7 passed 1 0.642
## Fst8 passed 1 0.451
## Fst9 passed 1 0.598
## Fst10 passed 1 0.226
## Fst11 passed 1 0.739
## Fst12 passed 1001 0.325
## Fst13 passed 1 0.544
## Fst14 passed 1 0.171
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.00296 9.09e-07
## Fst2 passed 0.02979 2.92e-06
## Fst3 passed 0.01379 1.90e-06
## Fst4 passed 0.01390 1.52e-06
## Fst5 passed 0.06218 4.78e-06
## Fst6 passed 0.45503 1.96e-05
## Fst7 passed 0.42998 1.97e-05
## Fst8 passed 0.00536 9.19e-07
## Fst9 passed 0.00211 6.36e-07
## Fst10 passed 0.00282 6.75e-07
## Fst11 passed 0.28637 1.44e-05
## Fst12 passed 0.09370 7.09e-06
## Fst13 passed 0.00422 2.15e-06
## Fst14 passed 0.00377 2.18e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3464.379 3011.465 2947.628 2940.803 3290.506 3517.191 3857.843 2551.180
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2745.558 3193.193 3252.568 3289.817 4418.774 4110.839plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000 1.00000000
## Lag 10 0.155833414 0.206810988 0.194777505 0.238179689 0.22078041
## Lag 50 0.016852494 0.006743189 0.024069595 0.022481998 0.03199996
## Lag 100 0.034147616 -0.008170037 -0.014832078 0.009286181 0.01404790
## Lag 500 -0.006903699 0.011510589 -0.005226999 -0.009888611 -0.02507923
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.00000000 1.00000000 1.00000000 1.000000000
## Lag 10 0.152771613 0.12874630 0.22094809 0.21536859 0.220335019
## Lag 50 -0.021530300 0.00542742 0.04804768 0.02625499 0.008744369
## Lag 100 0.007371122 0.04167123 0.01807723 -0.02233681 -0.020644073
## Lag 500 -0.007771695 -0.01183504 0.01694634 -0.01490340 0.017005151
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.000000000 1.0000000000 1.000000000
## Lag 10 0.211552877 0.206107671 0.0615100208 0.097395610
## Lag 50 0.007951997 0.008422355 -0.0008274452 -0.004911889
## Lag 100 -0.001314261 -0.012371428 -0.0118150949 -0.008606635
## Lag 500 -0.021475198 0.001311543 -0.0124779531 0.003365017levelplot(t(autocorr.diag(chain)))
plot(chain)
X temp
#read in trace file
chain <- read.table("~/Desktop/bayescenv.results/5.temp.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.2626
## Fst2 passed 1 0.2029
## Fst3 passed 1501 0.1513
## Fst4 passed 1 0.0667
## Fst5 passed 1 0.2337
## Fst6 passed 1 0.0786
## Fst7 passed 1 0.6822
## Fst8 passed 1 0.2935
## Fst9 passed 1 0.7620
## Fst10 passed 1 0.9486
## Fst11 passed 1 0.4700
## Fst12 passed 1 0.1575
## Fst13 passed 1001 0.0502
## Fst14 passed 1 0.4732
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.00538 2.04e-06
## Fst2 passed 0.02779 5.92e-06
## Fst3 passed 0.01146 4.29e-06
## Fst4 passed 0.00999 2.10e-06
## Fst5 passed 0.03249 5.31e-06
## Fst6 passed 0.58778 3.17e-05
## Fst7 passed 0.54927 3.36e-05
## Fst8 passed 0.01085 1.98e-06
## Fst9 passed 0.00450 1.15e-06
## Fst10 passed 0.00760 1.62e-06
## Fst11 passed 0.45822 2.76e-05
## Fst12 passed 0.23527 1.73e-05
## Fst13 passed 0.00962 4.81e-06
## Fst14 passed 0.01623 4.29e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2691.559 2097.295 2005.204 2771.339 3100.671 4198.742 4317.007 3047.346
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 3197.649 2945.189 3613.704 4389.094 3905.212 4374.512plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.000000000 1.00000000 1.000000000 1.00000000
## Lag 10 0.247083844 0.284414490 0.29144738 0.222265695 0.16371504
## Lag 50 0.003222022 0.032488281 0.04981576 0.034732693 0.01031547
## Lag 100 0.019122973 -0.002806995 0.01566395 0.006654658 -0.02529919
## Lag 500 -0.004043076 -0.018519751 -0.02412186 0.009981333 0.01007285
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.0000000000 1.000000000 1.00000000 1.00000000 1.000000000
## Lag 10 0.0869066826 0.073107064 0.18559709 0.19580414 0.158163428
## Lag 50 0.0078338922 -0.006113051 0.02074265 0.02771014 0.014531767
## Lag 100 0.0001182754 -0.011184935 0.01586157 -0.01977302 0.004750552
## Lag 500 -0.0017840469 0.010656763 0.01928425 -0.01313373 0.012312609
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.00000000 1.000000000 1.0000000000 1.000000000
## Lag 10 0.13644676 0.064866263 0.0920485316 0.041535224
## Lag 50 0.01810662 -0.003168005 -0.0004252882 0.018672300
## Lag 100 0.02157343 0.002090816 0.0121205276 -0.009688342
## Lag 500 -0.01283456 -0.015920269 -0.0089406902 0.005392791levelplot(t(autocorr.diag(chain)))
plot(chain)
2R precip
#read in trace file
chain <- read.table("~/Desktop/bayescenv.results/1.precip.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.0950
## Fst2 passed 1 0.6992
## Fst3 passed 1 0.2396
## Fst4 passed 1 0.1543
## Fst5 passed 1 0.8536
## Fst6 passed 1 0.7476
## Fst7 passed 1 0.1177
## Fst8 passed 1 0.6723
## Fst9 passed 1 0.5305
## Fst10 passed 1 0.4773
## Fst11 passed 1 0.1375
## Fst12 passed 1 0.7335
## Fst13 passed 1 0.0563
## Fst14 passed 1 0.2195
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.0204 2.05e-06
## Fst2 passed 0.0836 5.17e-06
## Fst3 passed 0.0474 3.62e-06
## Fst4 passed 0.0406 2.76e-06
## Fst5 passed 0.1550 8.19e-06
## Fst6 passed 0.4605 1.64e-05
## Fst7 passed 0.3994 1.58e-05
## Fst8 passed 0.0152 1.78e-06
## Fst9 passed 0.0140 1.50e-06
## Fst10 passed 0.0123 1.58e-06
## Fst11 passed 0.5002 1.45e-05
## Fst12 passed 0.1194 6.89e-06
## Fst13 passed 0.0129 2.14e-06
## Fst14 passed 0.0176 2.28e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2321.619 2269.473 2212.116 2548.863 2464.739 2909.981 3055.390 1745.441
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2177.850 1885.115 3015.433 2550.350 4217.141 4788.209plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.0000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.279035056 0.3491191559 0.316339429 0.324523910 0.298944818
## Lag 50 0.042700420 0.0199747514 0.046890794 -0.010406384 0.017838206
## Lag 100 -0.024328048 0.0008413047 0.007500330 -0.006107843 0.011860149
## Lag 500 -0.004804272 -0.0184593771 0.006677306 0.012839048 -0.004369266
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.00000000 1.000000000 1.00000000 1.00000000
## Lag 10 0.242538890 0.21558703 0.412727094 0.36520989 0.41293749
## Lag 50 0.003750541 -0.01254928 0.062369581 0.01625179 0.04022761
## Lag 100 0.004239439 0.01125442 0.019605666 -0.01625979 -0.01371651
## Lag 500 0.004150855 0.01632200 -0.002384758 0.01053419 0.02648554
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.0000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.2474054790 0.324262963 0.084736492 0.034856131
## Lag 50 0.0123022783 0.021086743 0.020605174 0.005192176
## Lag 100 -0.0007289595 -0.009306172 0.012728939 0.024543546
## Lag 500 -0.0114219891 0.023924651 -0.005707101 0.010621592levelplot(t(autocorr.diag(chain)))
plot(chain)
2L precip
#read in trace file
chain <- read.table("~/Desktop/bayescenv.results/2.precip.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.1466
## Fst2 passed 1 0.5393
## Fst3 passed 1 0.1785
## Fst4 passed 1 0.7888
## Fst5 passed 1 0.0654
## Fst6 passed 1 0.6292
## Fst7 passed 1 0.7924
## Fst8 passed 1 0.2902
## Fst9 passed 1 0.5813
## Fst10 passed 1 0.2968
## Fst11 passed 1 0.6113
## Fst12 passed 1 0.9727
## Fst13 passed 1 0.0682
## Fst14 passed 1 0.8447
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.0828 6.97e-06
## Fst2 passed 0.1118 8.00e-06
## Fst3 passed 0.0855 6.87e-06
## Fst4 passed 0.0504 4.43e-06
## Fst5 passed 0.1086 7.04e-06
## Fst6 passed 0.4386 1.81e-05
## Fst7 passed 0.3622 1.72e-05
## Fst8 passed 0.0633 5.41e-06
## Fst9 passed 0.0111 1.78e-06
## Fst10 passed 0.0216 2.53e-06
## Fst11 passed 0.4164 1.71e-05
## Fst12 passed 0.1576 9.94e-06
## Fst13 passed 0.0112 2.64e-06
## Fst14 passed 0.0243 3.38e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2325.429 2541.174 2410.012 2254.678 2868.554 3413.676 3627.546 2184.273
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 1948.314 2093.973 3026.689 2865.720 3780.262 4015.084plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.000000000 1.00000000 1.000000000 1.0000000000
## Lag 10 0.325335133 0.303912427 0.31622387 0.316553878 0.2429898481
## Lag 50 0.045236999 0.013360625 0.01468776 0.032365190 -0.0022162063
## Lag 100 0.002207994 -0.002298749 0.01197014 0.008727453 0.0054354023
## Lag 500 0.037239985 0.010289229 0.01334075 -0.012730006 0.0009585748
## Fst6 Fst7 Fst8 Fst9 Fst10 Fst11
## Lag 0 1.0000000000 1.000000000 1.000000000 1.00000000 1.00000000 1.00000000
## Lag 10 0.1873413072 0.158883076 0.372209870 0.37740652 0.37686383 0.24565568
## Lag 50 -0.0004343552 0.028203163 0.003878589 0.02675199 0.03762267 -0.01334104
## Lag 100 -0.0114548783 0.017476114 0.011309260 0.03459641 0.01180169 0.01325005
## Lag 500 -0.0033911565 0.002692615 0.012881890 0.01213164 0.01469708 0.02912510
## Fst12 Fst13 Fst14
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 10 0.2711540573 0.111114235 0.109054381
## Lag 50 0.0240830025 0.018676261 0.028990277
## Lag 100 0.0007908884 0.001585796 -0.008877302
## Lag 500 -0.0098078954 -0.011074998 0.018973711levelplot(t(autocorr.diag(chain)))
plot(chain)
3R precip
#read in trace file
chain <- read.table("~/Desktop/bayescenv.results/3.precip.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.0749
## Fst2 passed 1 0.4759
## Fst3 passed 1 0.9048
## Fst4 passed 1 0.5716
## Fst5 passed 1 0.4967
## Fst6 passed 501 0.0695
## Fst7 passed 1 0.9066
## Fst8 passed 1 0.6905
## Fst9 passed 1 0.4254
## Fst10 passed 1 0.5929
## Fst11 passed 1 0.4167
## Fst12 passed 1 0.4192
## Fst13 passed 1 0.9425
## Fst14 passed 1 0.9835
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.00362 9.29e-07
## Fst2 passed 0.03690 3.12e-06
## Fst3 passed 0.01746 1.81e-06
## Fst4 passed 0.02273 1.77e-06
## Fst5 passed 0.06793 4.94e-06
## Fst6 passed 0.49942 1.87e-05
## Fst7 passed 0.43812 1.86e-05
## Fst8 passed 0.00683 8.98e-07
## Fst9 passed 0.00427 8.17e-07
## Fst10 passed 0.00439 7.70e-07
## Fst11 passed 0.30194 1.33e-05
## Fst12 passed 0.08677 5.26e-06
## Fst13 passed 0.00582 1.83e-06
## Fst14 passed 0.00608 1.85e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2757.543 2486.818 2750.696 2949.755 2413.247 3180.299 3369.001 2639.708
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2316.895 2752.760 2918.441 3198.534 4268.424 4385.900plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.0000000000 1.000000000 1.000000000 1.00000000 1.000000000
## Lag 10 0.2074898958 0.294920773 0.251235605 0.25771369 0.271790253
## Lag 50 0.0004278046 0.027834296 0.002629771 0.01782778 0.028093022
## Lag 100 -0.0081154933 0.020992786 -0.001151065 -0.02079587 0.003812995
## Lag 500 -0.0341687512 0.002571793 0.005167771 -0.02314754 -0.013235729
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000 1.0000000000
## Lag 10 0.192336455 0.152377049 0.280683075 0.274224458 0.2416794838
## Lag 50 0.025310327 0.009760333 0.009364102 0.026609857 -0.0073825259
## Lag 100 -0.001719299 0.023882345 0.012818362 -0.009018276 -0.0029911294
## Lag 500 0.001545842 -0.009988508 -0.002701476 0.002842438 -0.0005693784
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.000000000 1.00000000 1.000000000
## Lag 10 0.266319381 0.219539856 0.07873323 0.065228713
## Lag 50 0.005403578 -0.013228305 -0.01359625 -0.022689042
## Lag 100 -0.025248772 0.003779874 -0.01682147 -0.007697065
## Lag 500 0.005995927 0.005560467 -0.01980415 -0.025335665levelplot(t(autocorr.diag(chain)))
plot(chain)
3L precip
#read in trace file
chain <- read.table("~/Desktop/bayescenv.results/4.precip.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.0593
## Fst2 passed 1 0.3878
## Fst3 passed 1501 0.1411
## Fst4 passed 1 0.7262
## Fst5 passed 1 0.0758
## Fst6 passed 1 0.1468
## Fst7 passed 1 0.4003
## Fst8 passed 1 0.1202
## Fst9 passed 1 0.3467
## Fst10 passed 1 0.1516
## Fst11 passed 1 0.7623
## Fst12 passed 1 0.0832
## Fst13 passed 1 0.1131
## Fst14 passed 1 0.2436
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.00377 1.00e-06
## Fst2 passed 0.03466 3.14e-06
## Fst3 passed 0.01666 2.54e-06
## Fst4 passed 0.01588 1.51e-06
## Fst5 passed 0.06978 5.06e-06
## Fst6 passed 0.45994 1.86e-05
## Fst7 passed 0.43354 1.92e-05
## Fst8 passed 0.00561 8.81e-07
## Fst9 passed 0.00249 7.23e-07
## Fst10 passed 0.00282 7.59e-07
## Fst11 passed 0.27781 1.44e-05
## Fst12 passed 0.09121 6.22e-06
## Fst13 passed 0.00380 1.95e-06
## Fst14 passed 0.00282 2.06e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 3303.063 3120.311 3099.876 3286.533 3475.422 3786.719 4004.836 3080.533
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 2737.336 3011.735 3264.489 3355.545 4614.069 4380.008plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.000000000 1.0000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.154442504 0.1911050674 0.190545771 0.148707939 0.179688678
## Lag 50 0.027457550 -0.0014849752 0.001936073 -0.015810083 0.012772222
## Lag 100 0.001185419 0.0213236072 0.008901381 0.026907068 -0.013881445
## Lag 500 -0.013719580 -0.0007043743 -0.015596771 0.002254101 0.006045462
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.000000000 1.00000000 1.00000000 1.000000000 1.000000000
## Lag 10 0.137884968 0.13343055 0.20720932 0.212499412 0.207790856
## Lag 50 0.014641027 -0.01086681 -0.01189910 -0.004713441 0.009122473
## Lag 100 0.002662847 -0.01623026 0.01399501 -0.008088975 0.003299975
## Lag 500 -0.004111614 -0.03393287 -0.02690997 0.010769362 -0.002593542
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.177060684 0.196617709 0.039942530 0.065897984
## Lag 50 0.006877828 -0.008366394 0.005346202 0.021497718
## Lag 100 0.008574525 0.005165060 0.004081798 -0.003552256
## Lag 500 0.015043778 -0.015157178 -0.005918661 0.006844219levelplot(t(autocorr.diag(chain)))
plot(chain)
X precip
#read in trace file
chain <- read.table("~/Desktop/bayescenv.results/5.precip.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain) #To test for convergence##
## Stationarity start p-value
## test iteration
## Fst1 passed 1 0.185
## Fst2 passed 1 0.544
## Fst3 passed 1 0.817
## Fst4 passed 1 0.444
## Fst5 passed 1 0.938
## Fst6 passed 1 0.343
## Fst7 passed 1 0.927
## Fst8 passed 1 0.645
## Fst9 passed 1 0.461
## Fst10 passed 1 0.429
## Fst11 passed 1 0.618
## Fst12 passed 1 0.874
## Fst13 passed 1 0.465
## Fst14 passed 1001 0.348
##
## Halfwidth Mean Halfwidth
## test
## Fst1 passed 0.00980 2.44e-06
## Fst2 passed 0.04032 6.37e-06
## Fst3 passed 0.01866 3.96e-06
## Fst4 passed 0.01717 3.00e-06
## Fst5 passed 0.04741 6.01e-06
## Fst6 passed 0.59466 3.18e-05
## Fst7 passed 0.55286 3.31e-05
## Fst8 passed 0.01239 2.19e-06
## Fst9 passed 0.00715 1.59e-06
## Fst10 passed 0.00886 1.88e-06
## Fst11 passed 0.33983 2.69e-05
## Fst12 passed 0.21633 1.77e-05
## Fst13 passed 0.00120 2.77e-06
## Fst14 passed 0.00628 3.80e-06effectiveSize(chain) #To compute effective sample size## Fst1 Fst2 Fst3 Fst4 Fst5 Fst6 Fst7 Fst8
## 2725.776 2775.216 2461.926 2987.504 3680.753 4098.921 4426.542 3248.299
## Fst9 Fst10 Fst11 Fst12 Fst13 Fst14
## 3241.979 3115.575 3647.149 4023.603 2069.145 3735.027plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))
## numeric(0)autocorr.diag(chain) #To look for auto-correlation## Fst1 Fst2 Fst3 Fst4 Fst5
## Lag 0 1.00000000 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.24415934 0.249295020 0.288593909 0.206370152 0.151778289
## Lag 50 0.03093667 -0.005745792 0.030530760 0.014825666 -0.022041685
## Lag 100 -0.02987944 -0.013353832 -0.001452828 0.003652313 0.009345431
## Lag 500 -0.03543258 -0.023356507 -0.006145148 0.019744592 -0.021071159
## Fst6 Fst7 Fst8 Fst9 Fst10
## Lag 0 1.0000000000 1.000000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.0988333468 0.060635120 0.176429411 0.205965864 0.198283150
## Lag 50 -0.0030011630 -0.007935019 -0.019761690 0.023252202 0.026701067
## Lag 100 -0.0001023968 0.016931640 -0.017285863 -0.029595664 0.003537505
## Lag 500 -0.0030713893 0.003075813 -0.004052689 0.009918183 0.015500000
## Fst11 Fst12 Fst13 Fst14
## Lag 0 1.00000000 1.000000000 1.000000000 1.000000000
## Lag 10 0.15625537 0.087244969 0.369219377 0.108982968
## Lag 50 -0.02331813 -0.029363383 0.043910244 -0.002319849
## Lag 100 0.01723236 0.012257628 0.032956149 -0.001070018
## Lag 500 0.03112715 -0.007413103 0.004300561 0.020137330levelplot(t(autocorr.diag(chain)))
plot(chain)
